Model-agnostic neural mean field with a data-driven transfer function
As one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the be...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
|
| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/ad787f |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582490907213824 |
|---|---|
| author | Alex Spaeth David Haussler Mircea Teodorescu |
| author_facet | Alex Spaeth David Haussler Mircea Teodorescu |
| author_sort | Alex Spaeth |
| collection | DOAJ |
| description | As one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable way to bridge the gap between single-neuron and population-level descriptions of neuronal activity, by modeling the behavior of a single representative neuron and extending this to the population. However, existing neural mean-field methods typically either take the limit of small interaction sizes, or are applicable only to the specific neuron models for which they were derived. This paper derives a mean-field model by fitting a transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. The transfer function is fitted numerically to simulated spike time data, and is entirely agnostic to the underlying neuronal dynamics. The resulting mean-field model predicts the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. Furthermore, it enables an accurate approximate bifurcation analysis as a function of the level of recurrent input. This model does not assume large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms. |
| format | Article |
| id | doaj-art-0511fef500fb479894ac7c65c140c3e7 |
| institution | Kabale University |
| issn | 2634-4386 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj-art-0511fef500fb479894ac7c65c140c3e72025-01-29T16:08:47ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862024-01-014303401310.1088/2634-4386/ad787fModel-agnostic neural mean field with a data-driven transfer functionAlex Spaeth0https://orcid.org/0000-0003-0702-3945David Haussler1Mircea Teodorescu2https://orcid.org/0000-0001-7085-5248Electrical and Computer Engineering Department, University of California , Santa Cruz, Santa Cruz, CA, United States of America; Genomics Institute, University of California , Santa Cruz, Santa Cruz, CA, United States of AmericaGenomics Institute, University of California , Santa Cruz, Santa Cruz, CA, United States of America; Biomolecular Engineering Department, University of California , Santa Cruz, Santa Cruz, CA, United States of AmericaElectrical and Computer Engineering Department, University of California , Santa Cruz, Santa Cruz, CA, United States of America; Genomics Institute, University of California , Santa Cruz, Santa Cruz, CA, United States of America; Biomolecular Engineering Department, University of California , Santa Cruz, Santa Cruz, CA, United States of AmericaAs one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable way to bridge the gap between single-neuron and population-level descriptions of neuronal activity, by modeling the behavior of a single representative neuron and extending this to the population. However, existing neural mean-field methods typically either take the limit of small interaction sizes, or are applicable only to the specific neuron models for which they were derived. This paper derives a mean-field model by fitting a transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. The transfer function is fitted numerically to simulated spike time data, and is entirely agnostic to the underlying neuronal dynamics. The resulting mean-field model predicts the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. Furthermore, it enables an accurate approximate bifurcation analysis as a function of the level of recurrent input. This model does not assume large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.https://doi.org/10.1088/2634-4386/ad787fneuronal dynamicsmean fieldtransfer functiondiffusion approximation |
| spellingShingle | Alex Spaeth David Haussler Mircea Teodorescu Model-agnostic neural mean field with a data-driven transfer function Neuromorphic Computing and Engineering neuronal dynamics mean field transfer function diffusion approximation |
| title | Model-agnostic neural mean field with a data-driven transfer function |
| title_full | Model-agnostic neural mean field with a data-driven transfer function |
| title_fullStr | Model-agnostic neural mean field with a data-driven transfer function |
| title_full_unstemmed | Model-agnostic neural mean field with a data-driven transfer function |
| title_short | Model-agnostic neural mean field with a data-driven transfer function |
| title_sort | model agnostic neural mean field with a data driven transfer function |
| topic | neuronal dynamics mean field transfer function diffusion approximation |
| url | https://doi.org/10.1088/2634-4386/ad787f |
| work_keys_str_mv | AT alexspaeth modelagnosticneuralmeanfieldwithadatadriventransferfunction AT davidhaussler modelagnosticneuralmeanfieldwithadatadriventransferfunction AT mirceateodorescu modelagnosticneuralmeanfieldwithadatadriventransferfunction |